Lung Segmentation from Chest X-rays using Variational Data Imputation
This work addresses automated risk scoring of COVID-19 from chest X-rays, but it is incremental as it builds on existing CNN and generative model techniques for a specific medical imaging task.
The paper tackled lung segmentation from abnormal chest X-rays with pulmonary opacifications by treating high opacity regions as missing data and using a modified CNN-based segmentation network with a deep generative model for data imputation, achieving results that extend to cases with extreme abnormalities.
Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19). Chest X-rays (CXRs) with such opacifications render regions of lungs imperceptible, making it difficult to perform automated image analysis on them. In this work, we focus on segmenting lungs from such abnormal CXRs as part of a pipeline aimed at automated risk scoring of COVID-19 from CXRs. We treat the high opacity regions as missing data and present a modified CNN-based image segmentation network that utilizes a deep generative model for data imputation. We train this model on normal CXRs with extensive data augmentation and demonstrate the usefulness of this model to extend to cases with extreme abnormalities.